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As societal demands on traffic control increase, traditional model-based traffic control methods are showing their limitations. This paper introduces a Distributed Adaptive Coordination Control (DACC) algorithm, which integrates data-driven control and multi-agent systems, aimed at optimizing traffic signal control across multiple intersections to effectively balance queuing times. Through joint simulation experiments with SUMO and Python, and comparison with the Proportional-Integral-Derivative Coordination Control (PIDCC) algorithm, the DACC algorithm has demonstrated advantages in reducing average queuing times and improving traffic flow efficiency. The results indicate that the DACC algorithm excels in managing traffic flow variations.

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Distributed Adaptive Coordination Control for Autonomous Road Traffic at Multiple Intersections

  • 会议论文

  • 发表日期：

    2025年

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  • Honghai Ji,
  • Qirui Shi,
  • Shida Liu,
  • Lingling Fan,
  • Li wang

摘要

As societal demands on traffic control increase, traditional model-based traffic control methods are showing their limitations. This paper introduces a Distributed Adaptive Coordination Control (DACC) algorithm, which integrates data-driven control and multi-agent systems, aimed at optimizing traffic signal control across multiple intersections to effectively balance queuing times. Through joint simulation experiments with SUMO and Python, and comparison with the Proportional-Integral-Derivative Coordination Control (PIDCC) algorithm, the DACC algorithm has demonstrated advantages in reducing average queuing times and improving traffic flow efficiency. The results indicate that the DACC algorithm excels in managing traffic flow variations.